In wind energy resource assessment, it is the primary goal to estimate the annual net energy that could be produced from a potential wind farm. This assessment includes several elements such as a wake loss model, long-term climatic adjustments, and a wind flow model. The wind flow model is the foundation of the wind resource assessment as it is used to estimate the free-stream (un-waked) wind speed distribution across the project area, which is then converted into gross annual energy production. If the wind flow model is flawed or biased, then all subsequent calculations will inherit those errors and the assessment will not be representative of the wind farm's true potential.
A wind flow model is developed by first taking measurements of the wind speed and direction typically at one or more meteorological tower (which could include a physical tower, or could include a remote sensing device such as a LIDAR or SODAR device) sites within the project boundaries. Characteristic wind measurements are collected with anemometers and wind vanes mounted typically at several levels on a tower or mast, called a meteorological (met) tower, or may be collected by a remote sensing device such as a SODAR or LIDAR. As used herein, a meteorological (met) tower is defined to mean any measurement of meteorological characteristics, whether from sensors mounted at one or more heights on a physical tower or from a remote sensing device such as a SODAR or a LIDAR. Prospective wind farm projects often have multiple met towers, although, in some cases, only one tower may be present within the project area. Often, the period of record for on-site measurements is not representative of long-term climatic conditions. To adjust on-site measurements to long-term conditions, project meteorological data is correlated to a long-term reference data set. On-site data is adjusted based on those correlations to reflect long-term climatic conditions. Then a joint frequency distribution of measured wind speed and wind direction is developed for each met tower. The joint frequency distribution is normalized for an average year. One of ordinary skill in the art can develop an accurate representation of climatology at a prospective wind farm meteorological tower site, thus the particulars are not discussed here. Further detail of the measurement and calculation of wind climatology can be found in Wind Characteristics, by Janardan Rohatgi and published by the Alternative Energy Institute, West Texas A&M University, 1994, incorporated herein by reference.
Measurements from met towers each represent one point in a project area, and not necessarily where wind turbines will be placed. To account for and predict the wind energy resource across a site, a wind flow model is used. Once characteristic representations of the wind climatology are developed for a particular met tower, the joint frequency distributions are used in a wind flow model to extrapolate and predict the wind energy resource spatially across the project area.
Wind flow across a given area is not typically consistent from point to point. On-site measurements typically show that there is spatial variation in wind speed and direction. Many aspects affect the variation in the wind regime, including trees, shrubs, buildings, and other surface “roughness” elements. Another aspect that affects the wind regime of a given site is the variation in terrain elevation, which is known as “terrain effects.” Analysis of wind regimes in areas with complex terrain with large differences in elevation across the site has demonstrated significant differences in the representative frequency distributions at different met towers, indicating that the terrain effects have a large influence upon the local wind climatology. In flatter sites, while the variation in measurements between met towers is smaller, it has been shown that terrain effects are still significant in affecting the spatial wind flow.
There are several different types of commercially-available wind flow models. All use a derived joint frequency distribution representing the climatology at the site of a met tower, elevation data, and other inputs, which can include surface roughness values and forest canopy heights. The wind flow models that are currently most commonly used include linear models and computational fluid dynamics (CFD) models. In general, linear models are viewed as simple and quick to produce estimates but are known to produce estimates with significant error, particularly in complex terrain. One of the most widely used linear models is the Wind Atlas Analysis and Application Program (WAsP), which was developed by Risø DTU, Denmark. WAsP has been documented to have significant error when used to predict the wind flow in sites with slopes more than 20 degrees, as detailed in WAsP Prediction Errors Due to Site Orography, by Anthony J. Bowen and Niels G. Mortensen, published by Risø National Laboratory, 2004, incorporated herein by reference.
On the other end of the spectrum, CFD models are considered to be more robust and can produce estimates with lower error. The most well-known CFD software models are Meteodyn and WindSim. CFD models are typically very complex and require extensive training, resources, and knowledge to use to accurately predict wind flow across a site.
Several validation studies have been conducted where linear and CFD models have been compared. In general, there have not been consistent results showing the superiority of either linear or CFD models. Some results that show the WAsP linear model performing as well as or better than the CFD models. However, some studies showed the CFD model producing a lower error than WAsP. In general, it is expected that CFD models should produce a more accurate wind flow model. However, this is not always the case, and the error in CFD models can be substantial.